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About
Brembo Solutions

  • A Cross-Industry fertilization
    Different fields have in common processes that can benefit from the same digital innovation. Automotive is from where we start to spread out our know-how across many industries.

    AI can be used for cross-industry applications by leveraging the power of Machine Learning algorithms to extract insights from meaningful and diverse datasets.
  • Building Cool things together
    A one-of-a-kind team, merging Data Science, AI, Data Processing and Software Engineering to develop ready-to-run Solutions for a global market. Our team mixes young enthusiasts and experienced talented professionals from various disciplines and cultures, working together to quickly achieve value creation. We range from the Silicon Valley to Nanjing, from statisticians to physicists and engineers.
  • Brembo Inspiration Lab
    Our US base to grasp cutting-edge AI technology. A center of excellence based in Silicon Valley, where our data science and domain experts experiment novel solutions. The right place to develop a network of strategic partnerships uncovering hidden synergies.
Digital Skills

Applied A.I.

We make proper blending of deep learning, machine learning, computer vision and NLP with your own business logic. Our solutions can both learn from large and small data sets, structured or unstructured, historical or live, depending on the real use case.

Human Interface

Mobile app development involves packing AI•Doing solutions in software applications for mobile devices such as smartphones and tablets, with the aim of providing users ubiquitous AI with various features and functions.

Data Analytics

The process of gathering, organizing, storing and analyzing your own data opens new routes to be discovered by potentially all areas in the company. Our ambition is to replicate our own experience in building knowledge upon data, as we did in the last decade with tangible results.

Generative A.I.

Generative AI (Gen AI) boosts our solutions by generating data, enhancing the quality and diversity of datasets. This improves the performance of AI models in applications like natural language processing, image recognition, and predictive analytics, leading to more robust and reliable outcomes.